We use the models of StyleGAN[1]} as our backbone.
For the semantic label classifier, we choose the DEX[2]} to classify the gender and age of the generated images.
There are several reasons for only to classify the gender and age aspects.
Firstly, these two features are the most common and important ones when describing faces.
Besides, as far as we know, there does not exist a classifier that can classify other fine-grained features of faces with high accuracy.
Although there are some models[3]}, [4]}, [5]} which are trained with CelebA[6]} dataset whose samples are labelled with 40 classes, the data imbalance issue and model architecture limits addressing multi-label classification.
Therefore, in this study, we only focus on controlling the age and gender rendering of the generated images.
As mentioned in Section 3, we can obtain different value ranges by the rules.
In practice, we label the age feature with "young", "middle-aged", and "old" semantic labels.
Therefore, we have six value ranges to sample from arranged as follow:
<TABLE>